ArrayMorph is a software to manage array data stored on cloud object storage efficiently. It supports both HDF5 C++ API and h5py API. The data returned by h5py API is numpy arrays. By using h5py API, users can access array data stored on the cloud and feed the read data into machine learning pipelines seamlessly.
The Camera Traps application is both a simulator and IoT device software for utilizing machine learning on the edge in field research. The first implementation specializes in applying computer vision (detection and classification) to wildlife images for animal ecology studies. Two operational modes are supported: "simulation" mode and "demo" mode. When executed in simulation mode, the software serves as a test bed for studying ML models, protocols and techniques that optimize storage, execution time, power and accuracy. It requires an input dataset of images to act as the images that would be generated an IoT camera device; it uses these images to drive the simulation.
The ctcontroller tool can be used to manage the provisioning and releasing of edge hardware as well as running and shutting down the camera-traps application.
The Cyberinfrastructure Knowledge Network (CKN) is an extensible and portable distributed framework designed to optimize AI at the edge—particularly in dynamic environments where workloads may change suddenly (for example, in response to motion detection). CKN enhances edge–cloud collaboration by using historical data, graph representations, and adaptable deployment of AI models to satisfy changing accuracy‑and‑latency demands on edge devices.
How ArrayMorph Works
Architectual Overview
Architecture Overview
CKN facilitates seamless connectivity between edge devices and the cloud through event streaming, enabling real‑time data capture and processing. By leveraging event‑stream processing, it captures, aggregates, and stores historical system‑performance data in a knowledge graph that models application behaviour and guides model selection and deployment at the edge.
Why use Ilúvatar for research?
Convention and Usage
At the heart of the Patra Knowledge Base is the concept of Model Cards. These cards are essentially detailed records that provide essential information about each AI/ML model. This information includes technical details like the model's accuracy and latency, but it goes beyond that to include non-technical aspects such as fairness, explainability, and the model's behavior in various deployment environments. This holistic approach is intended to create a comprehensive understanding of the model's strengths and weaknesses, enabling more informed decisions about its use and deployment
Features
Detailing csv columns
Details about the smart compiler can be found on the following diagramas:
FastKG is a knowledge graph embedding training library. Knowledge Graph (KG) embeddings are a way to represent entities and relationships from a KG in a continuous vector space, enabling tasks like link prediction and reasoning. TransE, a popular model, represents relationships as translations in the embedding space, such that for a valid triplet (head, relation, tail), the embedding of the head plus the relation vector is close to the embedding of the tail. Training data for TransE is typically stored in a tab-separated values (TSV) format, where each line represents a triplet, e.g., entity1\trelation1\tentity2. For example a dummy train.tsv should look like this:
Overview
Getting Started
See the full documentation for detailed instructions on creating custom plug‑ins and streaming events to the knowledge graph.
WAYS to configure HARP to setup applications for profiling:
System Requirements
Prerequisites
Install dependencies
Quick Start
Installation
Installation
Try it Out
Setting up cetus
Running the project
Ilúvatar is an open Serverless platform built with the goal of jumpstarting and streamlining FaaS research.
iSpLib is an accelerated sparse kernel library with PyTorch interface. This library has an auto-tuner which generates optimized custom sparse kernels based on the user environment. The goal of this library is to provide efficient sparse operations for Graph Neural Network implementations. Currently it has support for CPU-based efficient Sparse Dense Matrix Multiplication (spmm-sum only) with autograd.
The Patra Knowledge Base is a system designed to manage and track AI/ML models, with the objective of making them more accountable and trustworthy. It's a key part of the Patra ModelCards framework, which aims to improve transparency and accountability in AI/ML models throughout their entire lifecycle. This includes the model's initial training phase, subsequent deployments, and ongoing usage, whether by the same or different individuals.
The Patra Toolkit is a component of the Patra ModelCards framework designed to simplify the process of creating and documenting AI/ML models. It provides a structured schema that guides users in providing essential information about their models, including details about the model's purpose, development process, and performance. The toolkit also includes features for semi-automating the capture of key information, such as fairness and explainability metrics, through integrated analysis tools. By reducing the manual effort involved in creating model cards, the Patra Toolkit encourages researchers and developers to adopt best practices for documenting their models, ultimately contributing to greater transparency and accountability in AI/ML development.
This project introduces an agentic approach for high-level and multi-purpose compilers
Cetus Source to Source compiler improvements are being done at the University of Delaware. In this release, we release cetus base plus an static profiling feature and the instructions to use it.
1. Create a CKN Topic
SpMM Example
Set up Environment Variables
Run a simple example: Writing and Reading HDF5 files from Cloud
Create a Model Card
This Smart Compiler uses AI models and traditional compiler techniques to enhance the performance scalability of C programs and Python programs. By profiling, and finding approaches for optimizations.